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the raw chest X-ray images are denoised with a Gaussian filter during pre-processing followed by the Regions of Interest, and Edge Features are identified using Canny’s edge detector algorithm. Standardized Edge Features become the training inputs to a Dynamic Radial Basis Function Network classifier, developed from scratch. Results show that the developed classifier is 88% precise and 86% accurate in classifying the grade of illness with a much faster processing speed.

Cite this Paper:  Chattopadhyay, Subhagata. “Towards Grading Chest X-rays of COVID-19 Patients Using A Dynamic Radial Basis Function Network Classifier.” Artificial Intelligence Evolution (2021): 81-95.

Read COVID Xray Classification paper of Dr. Subhagata

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